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Dual-core silver-coated plasmonic sensor modeling with machine learning.

Chanchal Saha1, Farzana Haque1, Nazrul Islam2,3

  • 1Department of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Tangail-1902, Bangladesh.

Heliyon
|October 10, 2024
PubMed
Summary

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This summary is machine-generated.

This study presents a novel dual-core plasmonic sensor with enhanced sensitivity and resolution for precise analyte detection. Integrating machine learning algorithms further optimizes its performance for advanced bio-sensing applications.

Area of Science:

  • Plasmonics
  • Nanotechnology
  • Sensor Technology

Background:

  • Surface plasmon resonance (SPR) sensors are vital for sensitive, label-free detection.
  • Existing sensors face limitations in sensitivity and resolution for complex environments.

Purpose of the Study:

  • To develop a novel dual-core silver-coated plasmonic sensor with superior sensitivity and resolution.
  • To integrate machine learning (ML) algorithms for enhanced data analysis and predictive capabilities.

Main Methods:

  • Fabrication of a dual-core silver-coated plasmonic sensor.
  • Characterization of sensor performance across a refractive index (RI) range of 1.34 to 1.41.
  • Implementation of ML algorithms (MLR, SVR, RFR) for data analysis and prediction.
Keywords:
Dual-core silver-coatedMachine learningPCF-SPR sensorPlasmonic sensorSurface plasmon resonance

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Main Results:

  • Achieved highest reported wavelength sensitivity of 30,000 nm/RIU.
  • Demonstrated high resolution ( RIU) for precise analyte detection.
  • ML algorithms successfully predicted confinement loss and wavelength for various analytes.

Conclusions:

  • The dual-core plasmonic sensor offers significant advancements in sensitivity and resolution.
  • ML integration enhances sensor adaptability and data-driven insights.
  • This sensor is a promising tool for advanced bio-sensing applications.